Graph Neural Networks for Natural Language Processing.
Due to the large body of work on applying GNNs to NLP, we refer readers to a recent survey [1]} for a general review.
GNNs have been explored in several NLP tasks such as semantic role labeling [2]}, machine translation [3]}, and text classification [4]}, [5]}, [6]}, [7]}, [8]}.
GNNs have also been widely adopted in various variants of relation extraction on the sentence level, [9]}, [10]}, [11]}, the document level [12]}, [13]}, [14]}, [15]}, and the dialogue level [16]}.
However, on the sentence-level relation extraction, most existing work [9]}, [18]}, [19]} uses the graph neural networks to encode the relation representations from individual instances instead of operating the message passing between instances.
In contrast, we build a heterogeneous graph to connect the instances that share the properties for RE, and design the caching updater to efficiently perform the message passing between instances.
